Following most prior arts, we adopt mixed 2D-3D training on the MSCOCO [1]}, Human3.6M [2]}, MuCo-3DHP [3]}, and 3DPW [4]} datasets.
The pseudo ground-truth meshes of Human3.6M and MSCOCO are obtained by applying SMPLify-X [5]} to fit the joint annotations.
We evaluate our models on the Human3.6M, 3DPW, 3DPW-OCC [4]}, [7]}, and 3DOH [7]} testing sets.
Note that 3DOH is composed of images with object occlusions and 3DPW-OCC contains a subset of 3DPW sequences where the human bodies are partially occluded.
For quantitative evaluation, we calculate the common joint and vertex error metrics in the camera space and report them in millimeters (mm), including MPJPE (mean per-joint position error) [2]}, PA-MPJPE (Procrustes-aligned mean per-joint position error) [10]}, and MPVE (mean per-vertex error) [11]}.
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